When Arnecia Hawkins enrolled at Arizona State University last fall, she did not realize she was volunteering as a test subject in an experimental reinvention of American higher education. Yet here she was, near the end of her spring semester, learning math from a machine. In a well-appointed computer lab in Tempe, on Arizona State's desert resort of a campus, she and a sophomore named Jessica were practicing calculating annuities. Through a software dashboard, they could click and scroll among videos, text, quizzes and practice problems at their own pace. As they worked, their answers, along with reams of data on the ways in which they arrived at those answers, were beamed to distant servers. Predictive algorithms developed by a team of data scientists compared their stats with data gathered from tens of thousands of other students, looking for clues as to what Hawkins was learning, what she was struggling with, what she should learn next and how, exactly, she should learn it.

Having a computer for an instructor was a change for Hawkins. “I'm not gonna lie—at first I was really annoyed with it,” she says. The arrangement was a switch for her professor, too. David Heckman, a mathematician, was accustomed to lecturing to the class, but he had to take on the role of a roving mentor, responding to raised hands and coaching students when they got stumped. Soon, though, both began to see some benefits. Hawkins liked the self-pacing, which allowed her to work ahead on her own time, either from her laptop or from the computer lab. For Heckman, the program allowed him to more easily track his students' performance. He could open a dashboard that told him, in granular detail, how each student was doing—not only who was on track and who was not but who was working on any given concept. Heckman says he likes lecturing better, but he seems to be adjusting. One definite perk for instuctors: the software does most of the grading for them.

At the end of the term, Hawkins will have completed the last college math class she will probably ever have to take. She will think back on this data-driven course model—so new and controversial right now—as the “normal” college experience. “Do we even have regular math classes here?” she asks.

Big Data Takes Education

Arizona State's decision to move to computerized learning was born, at least in part, of necessity. With more than 70,000 students, Arizona State is the largest public university in the U.S. Like institutions at every level of American education, it is going through some wrenching changes. The university has lost 50 percent of its state funding over the past five years. Meanwhile enrollment is rising, with alarmingly high numbers of students showing up on campus unprepared to do college-level work. “There is a sea of people we're trying to educate that we've never tried to educate before,” says Al Boggess, director of the Arizona State math department. “The politicians are saying, ‘Educate them. Remediation? Figure it out. And we want them to graduate in four years. And your funding is going down, too.’”

Two years ago Arizona State administrators went looking for a more efficient way to shepherd students through basic general-education requirements—particularly those courses, such as college math, that disproportionately cause students to drop out. A few months after hearing a pitch by Jose Ferreira, the founder and CEO of the New York City adaptive-learning start-up Knewton, Arizona State made a big move. That fall, with little debate or warning, it placed 4,700 students into computerized math courses. Last year some 50 instructors coached 7,600 Arizona State students through three entry-level math courses running on Knewton software. By the fall of 2014 ASU aims to adapt six more courses, adding another 19,000 students a year to the adaptive-learning ranks. (In May, Knewton announced a partnership with Macmillan Education, a sister company to Scientific American.)